Differential Connectomics

Connectome can be used to identify and visualize major perturbed cell-cell communication pathways in A/B comparisons of complex tissue systems.

Load dependencies

library(Seurat)
library(SeuratData)
library(connectome)
library(ggplot2)
library(cowplot)
library(ComplexHeatmap)

Load Data

To demonstrate differential connectomics using Connectome, we will use the interferon-stimulated vs. control PBMC data distributed by SeuratData:

InstallData('ifnb')
data('ifnb')
table(Idents(ifnb))
#> 
#> IMMUNE_CTRL IMMUNE_STIM 
#>        6548        7451
Idents(ifnb) <- ifnb[['seurat_annotations']]
table(Idents(ifnb))
#> 
#>    CD14 Mono  CD4 Naive T CD4 Memory T    CD16 Mono            B 
#>         4362         2504         1762         1044          978 
#>        CD8 T  T activated           NK           DC  B Activated 
#>          814          633          619          472          388 
#>           Mk          pDC        Eryth 
#>          236          132           55

Scale and make connectomes

Connectomic networks must first be calculated for each tissue system separately. To do so:

# First identify ligands and receptors which have mapped in the dataset:
connectome.genes <- union(connectome::ncomms8866_human$Ligand.ApprovedSymbol,connectome::ncomms8866_human$Receptor.ApprovedSymbol)
genes <- connectome.genes[connectome.genes %in% rownames(ifnb)]
# Split the object by condition:
ifnb.list <- SplitObject(ifnb,split.by = 'stim')
# Normalize, Scale, and create Connectome:
ifnb.con.list <- list()
for (i in 1:length(ifnb.list)){
  ifnb.list[[i]] <- NormalizeData(ifnb.list[[i]])
  ifnb.list[[i]] <- ScaleData(ifnb.list[[i]],features = rownames(ifnb.list[[i]]))
  ifnb.con.list[[i]] <- CreateConnectome(ifnb.list[[i]],species = 'human',p.values = F)
}
names(ifnb.con.list) <- names(ifnb.list)

Make differential connectome

diff <- DifferentialConnectome(ifnb.con.list[[1]],ifnb.con.list[[2]])

Initial Scoring Plot

DifferentialScoringPlot(diff,min.score = 2,min.pct = 0.1,infinity.to.max = T)

Differential Circos Plots

Edgeweights here are the perturbation score, which is always positive, by design. However, in conenctomic data, there are 4 separate classes of perturbations, as both the ligand and the receptor can either be differentially increased or decreased. Here, we investigate each type of perturbation in turn:

Ligand and receptor are both UP:

diff.up.up <- subset(diff,ligand.norm.lfc > 0 & recept.norm.lfc > 0 )
CircosDiff(diff.up.up,min.score = 2,min.pct = 0.1,lab.cex = 0.4)

Ligand is UP and receptor is DOWN:

diff.up.down <- subset(diff,ligand.norm.lfc > 0 & recept.norm.lfc < 0 )
CircosDiff(diff.up.down,min.score = 2,min.pct = 0.1,lab.cex = 0.4)

Ligand is DOWN and receptor is UP:

diff.down.up <- subset(diff,ligand.norm.lfc < 0 & recept.norm.lfc > 0 )
CircosDiff(diff.down.up,min.score = 2,min.pct = 0.1,lab.cex = 0.4)

Ligand and receptor are both DOWN:

diff.down.down <- subset(diff,ligand.norm.lfc < 0 & recept.norm.lfc < 0 )
CircosDiff(diff.down.down,min.score = 2,min.pct = 0.1,lab.cex = 0.4)

Just specific cell interactions:

We can also look at perturbed edges within just a specific community of cells:

CircosDiff(diff,min.score = 2,min.pct = 0.1,lab.cex = 0.4,
           sources.include = c('pDC','CD8 T','B'),targets.include = c('CD16 Mono','CD14 Mono'))

Differential signaling in a specific cellular ‘niche’ due to perturbation:

CircosDiff(diff,min.score = 2,min.pct = 0.1,lab.cex = 0.4,
           targets.include = c('CD14 Mono'))

Can look at differential signaling hitting a single receptor:

CircosDiff(diff,min.pct = 0.1,lab.cex = 0.4,
           targets.include = c('CD14 Mono'),features = c('CCR5'))